AMD SME Benchmark Genoa
4th Gen AMD EPYC "Genoa" Secure Memory Encryption (SME) benchmarks by Michael Larabel for a future article.
HTML result view exported from: https://openbenchmarking.org/result/2212212-NE-AMDSMEBEN19.
QuantLib
High Performance Conjugate Gradient
NAS Parallel Benchmarks
Test / Class: BT.C
NAS Parallel Benchmarks
Test / Class: EP.C
NAS Parallel Benchmarks
Test / Class: FT.C
NAS Parallel Benchmarks
Test / Class: SP.C
miniBUDE
Implementation: OpenMP - Input Deck: BM1
miniBUDE
Implementation: OpenMP - Input Deck: BM1
miniBUDE
Implementation: OpenMP - Input Deck: BM2
miniBUDE
Implementation: OpenMP - Input Deck: BM2
Rodinia
Test: OpenMP LavaMD
Rodinia
Test: OpenMP CFD Solver
NAMD
ATPase Simulation - 327,506 Atoms
NWChem
Input: C240 Buckyball
Xcompact3d Incompact3d
Input: input.i3d 193 Cells Per Direction
OpenFOAM
Input: drivaerFastback, Small Mesh Size - Mesh Time
OpenFOAM
Input: drivaerFastback, Small Mesh Size - Execution Time
OpenRadioss
Model: Bumper Beam
OpenRadioss
Model: Cell Phone Drop Test
OpenRadioss
Model: INIVOL and Fluid Structure Interaction Drop Container
RELION
Test: Basic - Device: CPU
LULESH
Xmrig
Variant: Monero - Hash Count: 1M
Xmrig
Variant: Wownero - Hash Count: 1M
DaCapo Benchmark
Java Test: H2
Renaissance
Test: Finagle HTTP Requests
Renaissance
Test: In-Memory Database Shootout
Zstd Compression
Compression Level: 19, Long Mode - Compression Speed
Zstd Compression
Compression Level: 19, Long Mode - Decompression Speed
srsRAN
Test: OFDM_Test
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM
srsRAN
Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM
srsRAN
Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM
AOM AV1
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K
Embree
Binary: Pathtracer ISPC - Model: Crown
Kvazaar
Video Input: Bosphorus 4K - Video Preset: Very Fast
Kvazaar
Video Input: Bosphorus 4K - Video Preset: Ultra Fast
SVT-AV1
Encoder Mode: Preset 13 - Input: Bosphorus 4K
x264
Video Input: Bosphorus 4K
x265
Video Input: Bosphorus 4K
ACES DGEMM
Sustained Floating-Point Rate
Intel Open Image Denoise
Run: RTLightmap.hdr.4096x4096
OpenVKL
Benchmark: vklBenchmark ISPC
OSPRay
Benchmark: particle_volume/pathtracer/real_time
OSPRay
Benchmark: gravity_spheres_volume/dim_512/ao/real_time
7-Zip Compression
Test: Compression Rating
7-Zip Compression
Test: Decompression Rating
libavif avifenc
Encoder Speed: 2
libavif avifenc
Encoder Speed: 6
Timed Gem5 Compilation
Time To Compile
Timed Godot Game Engine Compilation
Time To Compile
Timed Linux Kernel Compilation
Build: defconfig
Timed Linux Kernel Compilation
Build: allmodconfig
Timed LLVM Compilation
Build System: Ninja
Timed LLVM Compilation
Build System: Unix Makefiles
OSPRay Studio
Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer
Liquid-DSP
Threads: 256 - Buffer Length: 256 - Filter Length: 57
Liquid-DSP
Threads: 384 - Buffer Length: 256 - Filter Length: 57
ASKAP
Test: tConvolve MPI - Degridding
ASKAP
Test: tConvolve MPI - Gridding
ASTC Encoder
Preset: Thorough
ASTC Encoder
Preset: Exhaustive
Graph500
Scale: 26
Graph500
Scale: 26
Graph500
Scale: 26
Graph500
Scale: 26
GROMACS
Implementation: MPI CPU - Input: water_GMX50_bare
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Only
TensorFlow
Device: CPU - Batch Size: 64 - Model: AlexNet
KTX-Software toktx
Settings: Zstd Compression 9
KTX-Software toktx
Settings: Zstd Compression 19
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
WRF
Input: conus 2.5km
GPAW
Input: Carbon Nanotube
Blender
Blend File: Classroom - Compute: CPU-Only
Blender
Blend File: Barbershop - Compute: CPU-Only
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
Xsbench
nginx
Connections: 500
ONNX Runtime
Model: super-resolution-10 - Device: CPU - Executor: Standard
Appleseed
Scene: Emily
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
Phoronix Test Suite v10.8.5